example

library(sf)
library(tmap)
library(leafpop)
library(leaflet)
library(tmaptools)
library(tidyverse)
library(plyr)

OSM <- st_read("D:/master/GIS/week5/OSM/gis_osm_pois_a_free_1.shp")

## Reading layer `gis_osm_pois_a_free_1' from data source `D:\master\GIS\week5\OSM\gis_osm_pois_a_free_1.shp' using driver `ESRI Shapefile'
## Simple feature collection with 35370 features and 4 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -0.5108706 ymin: 51.28117 xmax: 0.322123 ymax: 51.68948
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs

Londonborough <- st_read("D:/master/GIS/week5/statistical-gis-boundaries-london/ESRI/London_Borough_Excluding_MHW.shp")

## Reading layer `London_Borough_Excluding_MHW' from data source `D:\master\GIS\week5\statistical-gis-boundaries-london\ESRI\London_Borough_Excluding_MHW.shp' using driver `ESRI Shapefile'
## Simple feature collection with 33 features and 7 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 503568.2 ymin: 155850.8 xmax: 561957.5 ymax: 200933.9
## epsg (SRID):    NA
## proj4string:    +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.999601272 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defs

Airbnb <- read_csv("D:/master/GIS/week5/listings.csv")

## Parsed with column specification:
## cols(
##   id = col_double(),
##   name = col_character(),
##   host_id = col_double(),
##   host_name = col_character(),
##   neighbourhood_group = col_logical(),
##   neighbourhood = col_character(),
##   latitude = col_double(),
##   longitude = col_double(),
##   room_type = col_character(),
##   price = col_double(),
##   minimum_nights = col_double(),
##   number_of_reviews = col_double(),
##   last_review = col_date(format = ""),
##   reviews_per_month = col_double(),
##   calculated_host_listings_count = col_double(),
##   availability_365 = col_double()
## )

# plot xy data
Airbnb <- st_as_sf(Airbnb, coords = c("longitude", "latitude"), 
                   crs = 4326)

# reproject
OSM <- st_transform(OSM, 27700)
Airbnb <- st_transform(Airbnb, 27700)
# we don't need to reproject Londonborough, but it 
# doesn't have a CRS..you could also use set_crs
# it needs to have one for the next step
Londonborough<- st_transform(Londonborough, 27700)

#select hotels only
OSM <- OSM[OSM$fclass == 'hotel',]

# this bit of code would reduce our Airbnb data
# to entire places and available all year
Airbnb <- Airbnb[Airbnb$room_type == 'Entire home/apt' &
                   Airbnb$availability_365=='365',]

# make a function for the join
# functions are covered in practical 7
# but see if you can work out what is going on
# hint all you have to do is replace data1 and data2
# with the data you want to use

Joinfun <- function(data1, data2) {
  # join OSM and London boroughs
  joined <- st_join(data1, data2, join = st_within)
  
  # count the number of hotels per borough
  countno <- as.data.frame(count(joined$GSS_CODE))
  
  # join the count back to the borough layer
  counted <-left_join(data2, countno, by=c("GSS_CODE"="x"))
  
  return(counted)
}

# use the function for hotels
Hotels <- Joinfun(OSM, Londonborough)

# then for airbnb
Airbnb <- Joinfun(Airbnb, Londonborough)

# now try to arrange the plots with tmap
breaks = c(0, 5, 12, 26, 57, 286) 

#change the column name from freq for the legend
colnames(Hotels)[colnames(Hotels)=="freq"] <- "Accom count"


#join data
ti<-st_join(Airbnb, Hotels)
ti<-st_transform(ti,crs = 4326)

#remove the geometry for our pop up boxes to avoid
#the geometry field 
ti2<-ti
st_geometry(ti2)<-NULL
popairbnb=popupTable(ti2, zcol=c("NAME.x", "GSS_CODE.x", "freq"))
pophotels=popupTable(ti2, zcol=c("NAME.x", "GSS_CODE.x", "Accom count"))

tmap_mode("view")

## tmap mode set to interactive viewing

# set the colour palettes using our previously defined breaks
pal <- colorBin(palette = "YlOrRd", domain=ti2$freq, bins=breaks)
pal2 <- colorBin(palette = "YlOrRd", domain=ti2$`Accom count`, bins=breaks)

map<- leaflet(ti) %>%
  
  # add basemap options
  addTiles(group = "OSM (default)") %>%
  addProviderTiles(providers$Stamen.Toner, group = "Toner") %>%
  addProviderTiles(providers$Stamen.TonerLite, group = "Toner Lite") %>%
  addProviderTiles(providers$CartoDB.Positron, group = "CartoDB")%>%
  
  #add our polygons, linking to the tables we just made
  addPolygons(color="white", 
              weight = 2,
              opacity = 1,
              dashArray = "3",
              popup = popairbnb,
              fillOpacity = 0.7,
              fillColor = ~pal(freq),
              group = "Airbnb")%>%
  
  addPolygons(fillColor = ~pal(`Accom count`), 
              weight = 2,
              opacity = 1,
              color = "white",
              dashArray = "3",
              popup = pophotels,
              fillOpacity = 0.7,group = "Hotels")%>%
  # add a legend
  addLegend(pal = pal2, values = ~`Accom count`, group = c("Airbnb","Hotel"), 
            position ="bottomleft") %>%
  # specify layers control
  addLayersControl(
    baseGroups = c("OSM (default)", "Toner", "Toner Lite", "CartoDB"),
    overlayGroups = c("Airbnb", "Hotels"),
    options = layersControlOptions(collapsed = FALSE)
  )

## Warning in pal(`Accom count`): Some values were outside the color scale and
## will be treated as NA

# plot the map
map